CN115083198A - Multi-vehicle transport capacity resource scheduling method and device - Google Patents
Multi-vehicle transport capacity resource scheduling method and device Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
- G08G1/127—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams to a central station ; Indicators in a central station
- G08G1/13—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams to a central station ; Indicators in a central station the indicator being in the form of a map
Abstract
The invention provides a multi-vehicle transport capacity resource scheduling method and a device, wherein the method comprises the following steps: acquiring personnel waiting information, vehicle information and dynamic high-precision map information of each vehicle at each station in a target park; determining the station cost value of each station according to the personnel waiting information of each station, and determining the station with the largest station cost value as the current target station; determining vehicle cost values of all vehicles when reaching a current target site according to the vehicle information, and determining the vehicle with the minimum vehicle cost value as a target vehicle; and determining the driving path of the target vehicle when the target vehicle reaches the current target station according to the dynamic high-precision map information. By implementing the method and the system, the driving track of each vehicle is not fixed and unchanged, the driving track of each vehicle can be flexibly regulated according to the actual situation of each station, the traveling efficiency of personnel in the park is improved, and the vehicle with the minimum vehicle cost value is determined as the target vehicle, so that the utilization rate of the vehicles in the park is improved.
Description
Technical Field
The invention relates to the technical field of public transportation, in particular to a multi-vehicle transport capacity resource scheduling method and device.
Background
With the development of economy and urbanization, related operation enterprises begin to build more and more specific scene closed parks, such as large-scale sporting event parks, amusement park parks, industrial parks, scientific and technological parks, and the like, in order to efficiently utilize various resources and more conveniently manage places and personnel. The characteristics of these gardens are that the stream of people is intensive, can appear bigger crowd gush in and gush out the scene in morning and evening, and the scene is concentrated to the personnel of relative less scale also can appear in the middle period, for improving the intelligent progress of garden traffic, saves a large amount of manpowers, and a lot of gardens have been equipped with the autopilot garden vehicle, transport the passenger between the fixed station, through dispatching the garden vehicle to alleviate passenger flow pressure. However, the current scheduling method mainly faces to the public transportation environment in the city, the range is large, the distance between stations is long, the route is fixed, and a driver assists in feeding back the field situation. The relative range of the closed park is small, the distance between stations is short, and the visiting sequence of passengers is not fixed, so that the urban public transport system scheduling scheme cannot be directly adapted to unmanned buses in the park.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to overcome the defect that the public traffic scheduling scheme in the prior art cannot effectively relieve the pressure of the park passenger flow, thereby providing a multi-vehicle transportation capacity resource scheduling method and device.
The invention provides a multi-vehicle transport capacity resource scheduling method in a first aspect, which comprises the following steps: acquiring personnel waiting information, vehicle information and dynamic high-precision map information of each vehicle at each station in a target park; determining the station cost value of each station according to the personnel waiting information of each station, and determining the station with the largest station cost value as the current target station; determining vehicle cost values of all vehicles when reaching a current target site according to the vehicle information, and determining the vehicle with the minimum vehicle cost value as a target vehicle; and determining the driving path of the target vehicle when the target vehicle reaches the current target station according to the dynamic high-precision map information.
Optionally, in the method for scheduling multi-vehicle transportation capacity resources provided by the present invention, the waiting information of the staff includes: and determining the station cost value of each station according to the sum of weighted values of the per-person waiting time, the longest waiting time of the personnel and the personnel aggregation degree of each station.
Optionally, in the method for scheduling multi-vehicle transportation resources provided by the present invention, the vehicles include in-transit vehicles, the vehicle cost values of the vehicles arriving at the current target station are determined according to the vehicle information, and the vehicle with the minimum vehicle cost value is determined as the target vehicle, including: determining the vehicle cost value of each in-transit vehicle when the in-transit vehicle reaches the current target station according to the vehicle information of the in-transit vehicle; and if the vehicle cost value with the minimum value is less than or equal to the newly added vehicle cost value, determining the vehicle in transit corresponding to the vehicle cost value with the minimum value as the target vehicle.
Optionally, in the method for scheduling multi-vehicle capacity resources provided by the present invention, the vehicle includes parked vehicles, and if the minimum vehicle cost value is greater than the new departure vehicle cost value, one of the parked vehicles is determined as the target vehicle.
Optionally, in the method for scheduling multi-vehicle transportation resource provided by the present invention, the vehicle information includes vehicle remaining duration, vehicle load rate, vehicle type, and vehicle route station information, and the vehicle cost value of each vehicle when reaching the current target station is determined according to the weighted value of the vehicle remaining duration, vehicle load rate, vehicle type, and vehicle route station information of each vehicle.
Optionally, in the method for scheduling multi-vehicle transportation capacity resources provided by the present invention, the vehicle route station information includes the number of other stations spaced between the same stations of the vehicle in the current cycle, and the weighted value of the vehicle route station information is calculated by the following formula:
wherein，
n j Representing the number of other stations spaced between the same station of the jth group,
a weight representing vehicle route site information.
Optionally, in the method for scheduling resources of multi-vehicle transportation capability provided by the present invention, the dynamic high-precision map information includes lane topology information in the target park and congestion conditions of each road segment, and the determining of the driving path of the target vehicle when the target vehicle reaches the current target station according to the dynamic high-precision map information includes: determining a candidate path of a target vehicle to reach a current target station according to the lane topology information; if a plurality of candidate paths exist, determining the path length of each candidate path according to the lane topology information, and determining the running time of each candidate path according to the congestion condition of each road section; calculating the path cost value of each candidate path according to the path length and the driving duration of each candidate path; and determining the candidate path with the minimum path cost value as the driving path when the target vehicle reaches the current target station.
The second aspect of the present invention provides a multi-vehicle capacity resource scheduling device, including: the information acquisition module is used for acquiring personnel waiting information, vehicle information and dynamic high-precision map information of each station in the target park; the current target site determining module is used for determining the site cost value of each site according to the personnel waiting information of each site and determining the site with the largest site cost value as the current target site; the target vehicle determining module is used for determining the vehicle cost value of each vehicle when reaching the current target site according to the vehicle information and determining the vehicle with the minimum vehicle cost value as the target vehicle; and the running path determining module is used for determining a running path of the target vehicle when the target vehicle reaches the current target station according to the dynamic high-precision map information.
A third aspect of the present invention provides a computer apparatus comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to perform the method for scheduling multi-capacity resources according to the first aspect of the present invention.
A fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the method for scheduling multi-vehicular capacity resources according to the first aspect of the present invention.
The technical scheme of the invention has the following advantages:
the multi-vehicle transportation capacity resource scheduling method and the device provided by the invention can be used for acquiring the personnel waiting information of each station in a target park in real time, determining the station cost value of each station according to the personnel waiting information of each station, determining the station with the largest station cost value as the current target station and preferentially allocating vehicles for the current target station.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a specific example of a method for scheduling multi-vehicle capacity resources according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a specific example of a multi-vehicle capacity resource scheduling apparatus according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of a specific example of a computer device in the embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the technical features related to the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.
The embodiment of the invention provides a multi-vehicle transport capacity resource scheduling method, which comprises the following steps as shown in figure 1:
step S11: and acquiring personnel waiting information, vehicle information and dynamic high-precision map information of each vehicle at each station in the target park.
In an alternative embodiment, the person waiting information of each station refers to the person information waiting at each station. The personnel waiting information can be obtained through an accurate data acquisition mode or a data flow prediction mode.
In an optional embodiment, the accurate data obtaining mode is adopted to obtain the personnel waiting information of each site in the target park in real time, and the mode of obtaining the personnel waiting information of each site through the accurate data obtaining mode is as follows:
the method comprises the steps of obtaining position information of all personnel in a target park, determining sites where the personnel are located according to the position information of the personnel and the position information of the sites, and forming personnel waiting information of the sites according to the residence time of the personnel near the sites.
In an optional embodiment, when the person waiting information of each station is acquired through the accurate data acquisition mode, the person can manually input data such as current location, a nearest station, a destination site, and park exit information, so that the computer device can determine the person waiting information of each station when executing the multi-vehicle transportation capacity resource scheduling method provided by the embodiment of the invention.
In an optional embodiment, when the waiting information of the personnel at each station is acquired through the accurate data acquisition mode, data such as a campus people flow thermodynamic diagram and park entering data statistics can be acquired through hardware facilities of a target campus, so that the waiting information of the personnel at each station can be determined by computer equipment when the multi-vehicle transport capacity resource scheduling method provided by the embodiment of the invention is executed.
In an optional embodiment, when the personnel waiting information of each station is acquired through a data flow prediction mode, according to the statistical conditions of weather, holidays and park entering data, historical contemporaneous data and temporary data are combined, and the data model is used for simulating the personnel flow data in the park, so that the personnel waiting information of each station is determined.
The data flow prediction mode can be used as a prediction basis for people flow data at each moment of each site in the target park under the condition that no accurate people flow information is input; in the case of a campus-like designated app, the data model serves as an auxiliary prediction of campus traffic dynamics.
In an optional embodiment, the vehicle information and the dynamic high-precision map information are acquired in real time, and at different times, the vehicle information of the same running vehicle changes, and the dynamic high-precision map information also changes.
In an optional embodiment, compared with the traditional static high-precision map, the dynamic high-precision map increases the conditions of dynamic signal lights, tidal lanes, road blockage information, road control and the like. The dynamic high-precision map module provides dynamic information such as related signal lamps and the like for the capacity scheduling algorithm to influence the structure of the scheduling algorithm on the one hand, and provides topological relation among lanes as a basis for path planning on the other hand.
Step S12: and determining the station cost value of each station according to the personnel waiting information of each station, and determining the station with the largest station cost value as the current target station.
In an optional embodiment, the personnel waiting information of a station is determined by the personnel data waiting at the station, so that a station cost value calculated according to the personnel waiting information is used for representing the urgency of the station for the capacity resource demand, and the larger the station cost value is, the more capacity resources are required at the station to relieve the pressure of the station, so that the station with the largest station cost value is determined as the current target station, and the vehicle needs to preferentially arrive at the station.
In an optional embodiment, the personnel waiting information of each station is collected in real time, the cost value of each last station is calculated in real time according to the personnel waiting information, and the current target station is determined, and the current target station can be different at different times.
Step S13: and determining the vehicle cost value of each vehicle when reaching the current target station according to the vehicle information, and determining the vehicle with the minimum vehicle cost value as the target vehicle.
In an optional embodiment, for the same current target station, different vehicles are allocated to reach the station, the paid costs are different, the vehicle with the minimum vehicle cost value is determined as the target vehicle, and the target vehicle is allocated to reach the current target station, so that the use efficiency of the vehicles in the garden is improved.
Step S14: and determining the driving path of the target vehicle when the target vehicle reaches the current target station according to the dynamic high-precision map information.
In an optional embodiment, the driving path of the target vehicle when reaching the current target station can be determined according to Dijkstra, a ×, and other algorithms and combined with dynamic high-precision map information.
The multi-vehicle transportation capacity resource scheduling method provided by the embodiment of the invention can acquire the personnel waiting information of each site in the target park in real time, and the station cost value of each station is determined according to the personnel waiting information of each station, the station with the largest station cost value is determined as the current target station, and the vehicles are allocated to the current target station preferentially, therefore, in the embodiment of the invention, the driving track of each vehicle is not fixed and invariable, the driving track of each vehicle can be flexibly regulated and controlled according to the actual situation of each station by implementing the invention, the traveling efficiency of personnel in a garden is improved, and, in the multi-vehicle transportation capacity resource scheduling method provided by the embodiment of the invention, the vehicle cost value of each vehicle when arriving at the current target site is determined according to the vehicle information, and the vehicle with the minimum vehicle cost value is determined as the target vehicle, so that the utilization rate of the vehicles in the park is improved.
In an optional embodiment, in the method for scheduling multi-vehicle capacity resources provided in the embodiment of the present invention, the waiting information includes: the average waiting time of people, the longest waiting time of people and the personnel aggregation degree, wherein the station cost value of each station is determined according to the sum of weighted values of the average waiting time of people, the longest waiting time of people and the personnel aggregation degree of each station:
cost station_id ＝cost toruist_time +cost max_time +cost C ，
wherein, cost toruist_time Weight value, cost, representing the average waiting time of people max_time Weight value, cost, representing the maximum waiting time of a person C A weight value representing the degree of aggregation of the persons,
TIME tourist_i indicates the per-person waiting time, k, of the ith station tourist A weight representing the average person waiting time,
TIME max_i denotes the maximum waiting time, k, of the person at the ith station time_max Weight, cost, representing the longest waiting time of a person C ＝∑C i ·k C ，C i Indicates the degree of people gathering at the ith site, k C A weight representing the degree of people gathering.
In an alternative embodiment, the degree of people gathering is determined based on the number of people at the site.
In an alternative embodiment, the staff flow data between the stations is obtained through a precise data obtaining mode or a data flow prediction mode, and then the per-person waiting time, the longest staff waiting time and the staff gathering degree of each station are determined based on the staff flow data, as shown in the following table, the staff flow data is the number of staff moving from each station to other stations.
| 10:00 | Station A | Station B | Site C | Site D | … |
| Station A | - | 10 | 20 | 5 | |
| Station B | 5 | - | 10 | 12 | |
| Site C | - | ||||
| Site D | - | ||||
| … | … | … | … | … | … |
In an optional embodiment, in the method for scheduling multi-vehicle transportation capacity resources provided by the embodiment of the present invention, the vehicles in the target park include in-transit vehicles and parked vehicles, the in-transit vehicles are running vehicles, and the parked vehicles are non-departure vehicles. The step S13 specifically includes:
first, the vehicle cost value of each in-transit vehicle when arriving at the current target station is determined according to the vehicle information of the in-transit vehicle.
And then judging whether the vehicle cost value with the minimum value is less than the new vehicle sending cost value or not. In the embodiment of the invention, the newly added departure cost value refers to the vehicle cost value of dispatching the parking vehicle to reach the current target station.
And if the vehicle cost value with the minimum value is less than or equal to the newly-added vehicle cost value, determining the vehicle on the way corresponding to the vehicle cost value with the minimum value as the target vehicle.
And if the vehicle cost value with the minimum value is greater than the new vehicle sending cost value, determining one parked vehicle as the target vehicle.
In an optional embodiment, in the method for scheduling multi-vehicle transportation capability resources provided by the embodiment of the present invention, the vehicle information includes vehicle remaining duration, vehicle load rate, vehicle type, and vehicle route station information, and the vehicle cost value of each vehicle when reaching the current target station is determined according to the vehicle remaining duration, vehicle load rate, vehicle type, and weighted value of the vehicle route station information of each vehicle.
In an optional embodiment, in the method for scheduling multi-vehicle capacity resources provided by the embodiment of the present invention, the vehicle route station information includes the number of other stations spaced between the same stations of the vehicle in the current cycle, and the weighted value of the vehicle route station information is calculated by the following formula:
wherein the content of the first and second substances,
n j representing the number of other stations spaced between the same station of the jth group,
a weight representing vehicle route site information.
In an alternative embodiment, the vehicle cost value at the time the in-transit vehicle reaches the current destination is calculated by the following formula:
cost vel_id ＝cost soc_vel_id +cost load_vel_id +cost order_vel_id +cost class_vel_id ，
wherein, cost soc_vel_id Weight value, cost, representing the remaining range of the vehicle load_vel_id Weight value, cost, representing the load factor of the vehicle order_vel_id Indicating the way of a vehicleWeight value of radial station information, cost class_vel_id Weight value, cost, indicating the type of vehicle soc_vel_id ＝SOC vel_id ·k SOC ，SOC vel_id Indicating the remaining range of the vehicle, k SOC Weight, cost, representing the remaining range of the vehicle load_vel_id ＝LODA vel_id ·k LOAD ，LOAD vel_id Representing the vehicle load factor, k LOAD A weight representing a vehicle load rate.
In an alternative embodiment, the smaller the value of the remaining range of the vehicle, the greater the cost of continuing to use the vehicle.
In an alternative embodiment, the greater the vehicle load rate, indicating that the higher the utilization of the vehicle, the relatively smaller the weight.
In an alternative embodiment, in order to avoid the influence on the experience of tourists caused by frequent repeated roundtrips of vehicles in transit among the stations in the park, the weighted value of the vehicle routing station information is related to the number of other stations spaced among the repeated stations, and the purpose of avoiding the situation that the same station is frequently and repeatedly appeared is achieved as much as possible. For example, within one lifecycle of a vehicle, the site sequence of the approach can be represented as a- > b- > c- > d- > e, and according to the real-time planning result, it is possible to have a situation where the site repeatedly makes a round trip before the site occurs, for example, a- > b- > c- > b- > d- > c, according to the site requirement. In the station information, the station b and the station c appear a plurality of times. Two sites b are separated by only one site c and two sites b and d are separated between two sites c. The smaller the spacing between the same stations, the worse the passenger experience. According to the embodiment of the invention, the vehicle route station information is used as one of the conditions for scheduling the vehicle, so that the user experience is better, and the scheduling method is more humanized.
In an optional embodiment, in the method for scheduling multi-vehicle transportation capability resources provided in the embodiment of the present invention, the dynamic high-precision map information includes lane topology information in the target campus and congestion conditions of each road segment, and the step S14 specifically includes:
firstly, determining a candidate path of the target vehicle to the current target station according to the lane topology information.
And if the target vehicle only has one candidate path when reaching the current target station, determining the candidate path as the driving path of the target vehicle when reaching the current target station.
If a plurality of candidate paths exist, the following steps are executed:
firstly, determining the path length of each candidate path according to the lane topology information, and determining the running time of each candidate path according to the congestion condition of each road section.
In an alternative embodiment, the traffic condition of each road segment in the interior may be a traffic jam delay index, which is the time spent in a jam period/the time spent in a clear period.
Then, the route cost value of each candidate route is calculated according to the route length and the travel time length of each candidate route.
And finally, determining the candidate path with the minimum path cost value as the driving path of the target vehicle when the target vehicle reaches the current target station.
In an optional embodiment, in the method for scheduling multi-vehicle capacity resources provided in the embodiment of the present invention, the path cost value is calculated by the following formula:
wherein, BLOCK road_net Indicates the travel time period of the candidate route,
weight, ROUTE, representing the length of travel road_net The length of the path is indicated by,
a weight representing the path length.
An embodiment of the present invention provides a multi-vehicle transportation resource scheduling device, as shown in fig. 2, including:
the information acquisition module 21 is configured to acquire the waiting information of the staff at each station in the target park, the vehicle information of each vehicle, and the dynamic high-precision map information, for details, refer to the description of step S11 in the foregoing embodiment, which is not described herein again.
The current target site determining module 22 is configured to determine a site cost value of each site according to the person waiting information of each site, and determine a site with a largest site cost value as the current target site, for details, refer to the description of step S12 in the foregoing embodiment, and details are not described here again.
The target vehicle determining module 23 determines vehicle cost values of the vehicles arriving at the current target site according to the vehicle information, and determines the vehicle with the minimum vehicle cost value as the target vehicle, for details, refer to the description of step S13 in the above embodiment, and are not described herein again.
The driving path determining module 24 is configured to determine a driving path when the target vehicle reaches the current target station according to the dynamic high-precision map information, for details, refer to the description of step S14 in the foregoing embodiment, and are not described herein again.
An embodiment of the present invention provides a computer device, as shown in fig. 3, the computer device mainly includes one or more processors 31 and a memory 32, and one processor 31 is taken as an example in fig. 3.
The computer device may further include: an input device 33 and an output device 34.
The processor 31, the memory 32, the input device 33 and the output device 34 may be connected by a bus or other means, and fig. 3 illustrates the connection by a bus as an example.
The processor 31 may be a Central Processing Unit (CPU). The Processor 31 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The memory 32 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the multi-capacity resource scheduling device, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 32 may optionally include memory remotely located from the processor 31, and these remote memories may be connected to the multi-capacity resource scheduler via a network. The input device 33 may receive user input of a calculation request (or other numeric or character information) and generate key signal inputs associated with the multiple capacity resource scheduling device. The output device 34 may include a display device such as a display screen for outputting the calculation result.
Embodiments of the present invention provide a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and the computer-readable storage medium stores computer-executable instructions, where the computer-executable instructions may execute the method for scheduling multi-vehicle capacity resources in any of the above method embodiments. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications derived therefrom are intended to be within the scope of the invention.
Claims (10)
1. A multi-vehicle capacity resource scheduling method is characterized by comprising the following steps:
acquiring personnel waiting information, vehicle information and dynamic high-precision map information of each vehicle at each station in a target park;
determining the station cost value of each station according to the personnel waiting information of each station, and determining the station with the largest station cost value as the current target station;
determining vehicle cost values of all vehicles when the vehicles arrive at the current target station according to the vehicle information, and determining the vehicle with the minimum vehicle cost value as a target vehicle;
and determining a driving path of the target vehicle when the target vehicle reaches the current target station according to the dynamic high-precision map information.
2. The method of claim 1, wherein the personnel waiting information comprises: per-capita waiting time, longest waiting time for personnel, degree of personnel gathering,
and determining the station cost value of each station according to the sum of the per-person waiting time, the longest waiting time of the personnel and the weighted value of the personnel gathering degree of each station.
3. The method of claim 1, wherein the vehicles comprise in-transit vehicles,
determining vehicle cost values of all vehicles when reaching the current target station according to the vehicle information, and determining the vehicle with the minimum vehicle cost value as a target vehicle, wherein the vehicle cost values comprise:
determining the vehicle cost value of each in-transit vehicle when reaching the current target station according to the vehicle information of the in-transit vehicle;
and if the vehicle cost value with the minimum value is less than or equal to the newly added vehicle cost value, determining the vehicle in transit corresponding to the vehicle cost value with the minimum value as the target vehicle.
4. The method of claim 3, wherein the vehicles comprise parked vehicles,
and if the vehicle cost value with the minimum value is greater than the new vehicle sending cost value, determining one parked vehicle as the target vehicle.
5. The multi-vehicular capacity resource scheduling method according to claim 1, 3 or 4, wherein the vehicle information includes vehicle remaining duration, vehicle load rate, vehicle type, vehicle approach station information,
and determining the vehicle cost value of each vehicle when reaching the current target station according to the vehicle remaining duration, the vehicle load rate, the vehicle type and the weighted value of the vehicle approach station information of each vehicle.
6. The method of claim 5, wherein the vehicle routing site information includes the number of other sites spaced by the vehicle between the same sites in the current period, and the weighted value of the vehicle routing site information is calculated by the following formula:
wherein the content of the first and second substances,
n j representing the number of other stations spaced between the same station of the jth group,
a weight representing vehicle route site information.
7. The method according to claim 1, wherein the dynamic high-precision map information includes information on a topology of lanes in the target campus and a congestion status of each road segment,
determining a driving path of the target vehicle when the target vehicle reaches the current target station according to the dynamic high-precision map information, wherein the method comprises the following steps:
determining a candidate path of the target vehicle to the current target station according to the lane topology information;
if a plurality of candidate paths exist, determining the path length of each candidate path according to the lane topology information, and determining the running time of each candidate path according to the congestion condition of each road section;
calculating the path cost value of each candidate path according to the path length and the running time of each candidate path;
and determining the candidate path with the minimum path cost value as a driving path when the target vehicle reaches the current target station.
8. A multi-capacity resource scheduling apparatus, comprising:
the information acquisition module is used for acquiring personnel waiting information, vehicle information and dynamic high-precision map information of each station in the target park;
the current target site determining module is used for determining the site cost value of each site according to the personnel waiting information of each site and determining the site with the largest site cost value as the current target site;
the target vehicle determining module is used for determining vehicle cost values of all vehicles when the vehicles arrive at the current target station according to the vehicle information and determining the vehicle with the minimum vehicle cost value as a target vehicle;
and the running path determining module is used for determining the running path of the target vehicle when the target vehicle reaches the current target station according to the dynamic high-precision map information.
9. A computer device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to perform the method of scheduling multi-capacity resources of any of claims 1-7.
10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the method of scheduling multi-capacity resources of any one of claims 1-7.
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| CN202210678890.6A CN115083198B (en) | 2022-06-15 | 2022-06-15 | Multi-vehicle power resource scheduling method and device |
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